Voltage Regulation Planning Based on Optimal Grid-Connected Renewable Energy Allocation Using Nature-Inspired Algorithms to Reduce Switching Cycles of On-Load Tap Changing Transformer

Abstract

Due to the non-deterministic and probabilistic qualities of renewable energy resources (RER), integrating these resources has posed a challenge for energy companies and government policy planners. Hybrid power systems require frequent voltage regulation to address the changes in renewable generation and facilitate voltage management. This leads to an increment in the operating time of the tap changer in the power transformer, thus resulting in accelerated degradation of the on-load tap changer (OLTC) device. The primary focus of this paper is to optimally manage the penetration level of renewable generation while maintaining the minimum time for proper operation of voltage regulation devices. Accordingly, the major contribution of this paper is to address the switching cycle issues of the on-load tap-changing transformer. Initially, a framework is proposed to optimize the location and size of renewable power generation using a multi-objective cuckoo search optimization algorithm. The main goal here is to reduce power losses and tap changer operations while improving the system voltage profile under different levels of renewable energy penetration. The optimization problem takes into consideration the fluctuating nature of wind speed and solar irradiance for sunny and cloudy days over 24 h. The framework has been applied to the IEEE 57-bus and 118-bus systems with different load levels. The performance of the proposed approach has been benchmarked by comparing it with the genetic optimization algorithm to identify the higher potential buses for renewable generation allocation. The cuckoo search algorithm (CSA) shows outstanding results in reducing the number of switching cycles to about (37 – 43) % whereas the genetic algorithm (GA) only (17 – 18). In this sense, the number of changes in tap position when using the CSA is 1956 for sunny days and 1763 for cloudy days compared to GA 2558 for sunny days and 2547 for cloudy days. The voltage profiles for both algorithms are maintained in the range of (1.06 and 0.94) per unit. The results affirm the effectiveness of the proposed approach in determining the optimal placement and size of RER with voltage profile improvement and reduction in the switching cycles of OLTC.

Keywords:

• optimal voltage regulation
• renewable energy resources
• on-load tap changer
• multi-objective optimization
• genetic algorithm
• Cuckoo Search Algorithm

ACKNOWLEDGEMENTS

The authors would like to thank the Universiti Malaysia Sarawak for providing all facilities to carry out this research and wish also to thank the editor and reviewers for the invaluable time allotted to review this paper.

Additional information

Funding

The authors gratefully acknowledge financial support from the Ministry of Higher Education (Malaysia) under the Fundamental Research Grant Scheme (FRGS/1/2021/TK0/UNIMAS/02/3).

Notes on contributors

Hamid K. Ali

Hamid K. Ali, Ph.D. student at the department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Malaysia Sarawak.

Ahmed M. A. Haidar

Ahmed M. A. Haidar, Associate Professor in Power Engineering, Faculty of Engineering Universiti Malaysia Sarawak, and Adjunct Associate Professor, School of Engineering, University of Southern Queensland.

Norhuzaimin Julai

Norhuzaimin Julai, Associate Professor in Electrical & Electronic Engineering; Deputy Dean (Research & Commercialization), Faculty of Engineering, Universiti Malaysia Sarawak.

Andreas Helwig

Andreas Helwig, Associate Professor Electro-Mechanical Engineering; Post-graduate Program Director, School of Engineering, University of Southern Queensland.